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treenode.c
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/* treenode.c - Functions for hierarchical word distributions. */
/* Copyright (C) 1997, 1998, 1999 Andrew McCallum
Written by: Andrew Kachites McCallum <mccallum@cs.cmu.edu>
This file is part of the Bag-Of-Words Library, `libbow'.
This library is free software; you can redistribute it and/or
modify it under the terms of the GNU Library General Public License
as published by the Free Software Foundation, version 2.
This library is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
Library General Public License for more details.
You should have received a copy of the GNU Library General Public
License along with this library; if not, write to the Free Software
Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111, USA */
#include <bow/libbow.h>
#include <argp.h>
#include <bow/crossbow.h>
#define USE_ACCELERATED_EM 0
#define MISC_STAYS_FLAT 1
/* Accellerated EM: still gauranteed to converge below 2.0. 1.8 is good */
#define EM_ACCELERATION 1.8
/* Functions for creating, reading, writing the treenode */
/* Create and return a new treenode, adding it as a child of PARENT,
if PARENT is non-NULL. */
treenode *
bow_treenode_new (treenode *parent,
int children_capacity,
const char *name)
{
treenode *ret;
int i;
/* To avoid malloc'ing zero bytes */
if (children_capacity == 0)
children_capacity = 1;
ret = bow_malloc (sizeof (treenode));
/* Set relationship with parent. */
ret->parent = parent;
if (parent)
{
ret->ci_in_parent = parent->children_count;
if (parent->children_count >= parent->children_capacity)
{
parent->children_capacity *= 2;
parent->children =
bow_realloc (parent->children,
parent->children_capacity * sizeof (void*));
}
parent->children[parent->children_count++] = ret;
if (name)
{
/* +1 for the /, +1 for the terminating 0 */
ret->name = bow_malloc (strlen (parent->name) + strlen (name) + 2);
sprintf ((char*)ret->name, "%s%s/", parent->name, name);
}
else
{
ret->name = bow_malloc (strlen (parent->name) + 20);
sprintf ((char*)ret->name, "%s%d/", parent->name, ret->ci_in_parent);
}
ret->words_capacity = parent->words_capacity;
ret->classes_capacity = parent->classes_capacity;
}
else
{
ret->ci_in_parent = -1;
ret->name = strdup ("/");
ret->words_capacity = bow_num_words ();
ret->classes_capacity = 0;
}
ret->children_count = 0;
ret->children_capacity = children_capacity;
assert (children_capacity);
ret->children = bow_malloc (ret->children_capacity * sizeof (void*));
ret->words = bow_malloc (ret->words_capacity * sizeof (double));
ret->new_words = bow_malloc (ret->words_capacity * sizeof (double));
ret->new_words_normalizer = 0;
for (i = 0; i < ret->words_capacity; i++)
{
ret->words[i] = 0;
ret->new_words[i] = 0;
}
ret->prior = 1.0;
ret->new_prior = 1.0;
if (parent)
ret->depth = parent->depth + 1;
else
ret->depth = 0;
/* Initialize ancestor mixture weights, LAMBDAS, to use exclusively
the local estimate. */
ret->lambdas = bow_malloc ((ret->depth + 2) * sizeof (double));
ret->new_lambdas = bow_malloc ((ret->depth + 2) * sizeof (double));
ret->lambdas[0] = 1.0;
ret->new_lambdas[0] = 1.0;
for (i = 1; i < ret->depth + 2; i++)
{
ret->lambdas[i] = 0;
ret->new_lambdas[i] = 0;
}
/* Initialize the CLASSES distribution later, only if requested. */
if (ret->classes_capacity == 0)
{
ret->classes = NULL;
ret->new_classes = NULL;
}
else
{
ret->classes = bow_malloc (ret->classes_capacity * sizeof (double));
ret->new_classes = bow_malloc (ret->classes_capacity * sizeof (double));
for (i = 0; i < ret->classes_capacity; i++)
{
ret->classes[i] = 1.0 / ret->classes_capacity;
ret->new_classes[i] = 0.0;
}
}
/* Initialize the DI_WI_NEW_WORDS later, only if requested. */
ret->di_loo = NULL;
ret->di_wvi_loo = NULL;
ret->new_di_loo = NULL;
ret->new_di_wvi_loo = NULL;
return ret;
}
/* Free the memory allocate by TN and its children */
void
bow_treenode_free (treenode *tn)
{
int ci;
assert (tn == NULL && tn->ci_in_parent == -1);
for (ci = 0; ci < tn->children_count; ci++)
bow_treenode_free (tn->children[ci]);
if (tn->children) bow_free (tn->children);
if (tn->words) bow_free (tn->words);
if (tn->new_words) bow_free (tn->new_words);
if (tn->lambdas) bow_free (tn->lambdas);
if (tn->new_lambdas) bow_free (tn->new_lambdas);
if (tn->name) bow_free ((char*)tn->name);
bow_free (tn);
}
/* Create and return a new treenode with the proper settings to be the
root treenode */
treenode *
bow_treenode_new_root (int children_count)
{
return bow_treenode_new (NULL, children_count, NULL);
}
/* Reallocate memory for the WORDS and NEW_WORDS arrays, big enough
for the vocabulary of size bow_num_words(). This is useful when the
tree has been created before all the documents have been indexed. */
void
bow_treenode_realloc_words_all (treenode *root)
{
int i;
if (bow_num_words () > root->words_capacity)
{
root->words_capacity = bow_num_words ();
root->words =
bow_realloc (root->words, root->words_capacity * sizeof (double));
root->new_words =
bow_realloc (root->words, root->words_capacity * sizeof (double));
root->new_words_normalizer = 0;
for (i = 0; i < root->words_capacity; i++)
{
root->words[i] = 0;
root->new_words[i] = 0;
}
}
for (i = 0; i < root->children_count; i++)
bow_treenode_realloc_words_all (root->children[i]);
}
/* Add to parent a CHILD that was previously created with a NULL parent. */
void
bow_treenode_add_child (treenode *parent, treenode *child)
{
assert (parent->children_count < parent->children_capacity);
assert (child->ci_in_parent == -1);
child->parent = parent;
child->ci_in_parent = parent->children_count;
parent->children[parent->children_count++] = child;
}
/* Detach CHILD from PARENT, shifting remaining children, and updating
the remaining children's CI_IN_PARENT. */
void
bow_treenode_remove_child (treenode *parent, treenode *child)
{
bow_error ("%s: Not yet implemented", __PRETTY_FUNCTION__);
}
/* To this node and all its children, add a child named "Misc" if not
there already. */
void
bow_treenode_add_misc_child_all (treenode *root)
{
int ci;
/* If ROOT is a leaf, just return immediately */
if (root->children_count == 0)
return;
/* Search for a pre-existing "Misc" child */
for (ci = 0; ci < root->children_count; ci++)
if (strstr (root->children[ci]->name, "/Misc/"))
goto do_children;
/* Add a "Misc" child */
bow_treenode_new (root, 0, "Misc");
/* Recursively handle children */
do_children:
for (ci = 0; ci < root->children_count; ci++)
bow_treenode_add_misc_child_all (root->children[ci]);
}
/* Return the "next" treenode in a traversal of the tree. CONTEXT
should be initialized to the root of the tree, otherwise strange
results will ensue: nodes on the path from the initial CONTEXT node
to the root will be skipped by the iteration. When the traversal
is finished, NULL will be returned. */
treenode *
bow_treenode_iterate_all (treenode **context)
{
treenode *ret;
if (*context == NULL)
return NULL;
/* Save the context as this call's return value */
ret = *context;
/* Update the context for the next call. */
if ((*context)->children_count)
*context = (*context)->children[0];
else
{
while ((*context)->parent
&& ((*context)->ci_in_parent
== (*context)->parent->children_count-1))
{
*context = (*context)->parent;
}
if ((*context)->parent)
*context = (*context)->parent->children[(*context)->ci_in_parent+1];
else
*context = NULL;
}
return ret;
}
/* Same as above, but only return the leaf nodes. */
treenode *
bow_treenode_iterate_leaves (treenode **context)
{
treenode *ret;
while ((ret = bow_treenode_iterate_all (context))
&& ret->children_count != 0)
;
return ret;
}
treenode *
bow_treenode_iterate_all_under_node (treenode **context, treenode *node)
{
treenode *ret;
if (*context == NULL)
return NULL;
/* Save the context as this call's return value */
ret = *context;
/* Update the context for the next call. */
if ((*context)->children_count)
*context = (*context)->children[0];
else
{
while ((*context)->parent != node
&& ((*context)->ci_in_parent
== (*context)->parent->children_count-1))
{
*context = (*context)->parent;
}
if ((*context)->parent != node)
*context = (*context)->parent->children[(*context)->ci_in_parent+1];
else
*context = NULL;
}
return ret;
}
/* Same as above, but only return the leaf nodes. */
treenode *
bow_treenode_iterate_leaves_under_node (treenode **context, treenode *node)
{
treenode *ret;
while ((ret = bow_treenode_iterate_all_under_node (context, node))
&& ret->children_count != 0)
;
return ret;
}
/* Return the deepest descendant with a ->NAME that is contained in NAME */
treenode *
bow_treenode_descendant_matching_name (treenode *root, const char *name)
{
int ci;
treenode *tr;
#if WHIZBANG
char buf[256];
strcpy (buf, "./data");
strcat (buf, root->name);
if (strstr (name, buf) != name)
#else
if (!strstr (name, root->name))
return NULL;
#endif
for (ci = 0; ci < root->children_count; ci++)
{
if ((tr = bow_treenode_descendant_matching_name
(root->children[ci], name)))
return tr;
}
#if WHIZBANG
assert (root->depth == 2);
#endif
return root;
}
/* Archiving */
#define BOW_TREENODE_HEADER_STRING "treenode\n"
/* Write a treenode (and all its children) to FP. */
void
bow_treenode_write (treenode *tn, FILE *fp)
{
int i;
/* Write a tag that will later help verify we are reading correctly. */
bow_fwrite_string (BOW_TREENODE_HEADER_STRING, fp);
/* If TN is NULL, write a 0 and return */
if (tn)
bow_fwrite_int (1, fp);
else
{
bow_fwrite_int (0, fp);
return;
}
/* Write the name */
bow_fwrite_string (tn->name, fp);
/* Write the multinomial */
bow_fwrite_int (tn->words_capacity, fp);
bow_fwrite_double (tn->new_words_normalizer, fp);
for (i = 0; i < tn->words_capacity; i++)
bow_fwrite_double (tn->words[i], fp);
/* Write the prior */
bow_fwrite_double (tn->prior, fp);
bow_fwrite_double (tn->new_prior, fp);
/* Write the lambda mixture weights */
bow_fwrite_int (tn->depth, fp);
for (i = 0; i < tn->depth + 2; i++)
{
bow_fwrite_double (tn->lambdas[i], fp);
bow_fwrite_double (tn->new_lambdas[i], fp);
}
/* Write the class distribution */
bow_fwrite_int (tn->classes_capacity, fp);
if (tn->classes_capacity)
{
for (i = 0; i < tn->classes_capacity; i++)
bow_fwrite_double (tn->classes[i], fp);
}
/* Write the children treenodes */
bow_fwrite_int (tn->children_count, fp);
bow_fwrite_int (tn->children_capacity, fp);
bow_fwrite_int (tn->ci_in_parent, fp);
for (i = 0; i < tn->children_count; i++)
bow_treenode_write (tn->children[i], fp);
}
/* Read and return a new treenode (and all its children) from FP. */
treenode *
bow_treenode_new_from_fp (FILE *fp)
{
char *header;
treenode *tn;
int i;
/* Verify that we are starting read from the correct place in the FP */
bow_fread_string (&header, fp);
if (strcmp (header, BOW_TREENODE_HEADER_STRING) != 0)
bow_error ("Trying to read a treenode from bad FILE* location");
bow_free (header);
/* If a NULL treenode was written, return NULL. */
bow_fread_int (&i, fp);
if (i == 0)
return NULL;
tn = bow_malloc (sizeof (treenode));
tn->parent = NULL;
/* Read the name */
bow_fread_string ((char**)&(tn->name), fp);
/* Read the multinomial */
bow_fread_int (&(tn->words_capacity), fp);
tn->words = bow_malloc (tn->words_capacity * sizeof (double));
tn->new_words = bow_malloc (tn->words_capacity * sizeof (double));
bow_fread_double (&(tn->new_words_normalizer), fp);
for (i = 0; i < tn->words_capacity; i++)
{
bow_fread_double (&(tn->words[i]), fp);
tn->new_words[i] = 0;
}
/* Read the prior */
bow_fread_double (&(tn->prior), fp);
bow_fread_double (&(tn->new_prior), fp);
/* Read the lambda mixture weights */
bow_fread_int (&(tn->depth), fp);
tn->lambdas = bow_malloc ((tn->depth + 2) * sizeof (double));
tn->new_lambdas = bow_malloc ((tn->depth + 2) * sizeof (double));
for (i = 0; i < tn->depth + 2; i++)
{
bow_fread_double (&(tn->lambdas[i]), fp);
bow_fread_double (&(tn->new_lambdas[i]), fp);
}
/* Read in the class distribution */
bow_fread_int (&(tn->classes_capacity), fp);
if (tn->classes_capacity)
{
tn->classes = bow_malloc (tn->classes_capacity * sizeof (double));
tn->new_classes = bow_malloc (tn->classes_capacity * sizeof (double));
for (i = 0; i < tn->classes_capacity; i++)
{
bow_fread_double (&(tn->classes[i]), fp);
tn->new_classes[i] = 0;
}
}
else
tn->classes = tn->new_classes = NULL;
/* Read the children treenodes */
bow_fread_int (&(tn->children_count), fp);
bow_fread_int (&(tn->children_capacity), fp);
tn->children = bow_malloc (tn->children_capacity * sizeof (void*));
bow_fread_int (&(tn->ci_in_parent), fp);
for (i = 0; i < tn->children_count; i++)
{
tn->children[i] = bow_treenode_new_from_fp (fp);
tn->children[i]->parent = tn;
assert (tn->children[i]->ci_in_parent == i);
}
/* Initialize the DI_WI_NEW_WORDS later, only if requested. */
tn->di_loo = NULL;
tn->di_wvi_loo = NULL;
tn->new_di_loo = NULL;
tn->new_di_wvi_loo = NULL;
//bow_verbosify (bow_progress, "Read treenode %s\n", tn->name);
return tn;
}
/* Set all of TN's ancestor mixture weights, LAMBDAS, to equal values. */
void
bow_treenode_set_lambdas_uniform (treenode *tn)
{
int i;
double lambda = 1.0 / (tn->depth + 2);
for (i = 0; i < tn->depth + 2; i++)
tn->lambdas[i] = lambda;
}
/* Same as above, but for all leaves in the tree. */
void
bow_treenode_set_lambdas_uniform_all (treenode *tn)
{
treenode *iterator, *leaf;
assert (tn->parent == NULL);
for (iterator = tn; (leaf = bow_treenode_iterate_leaves (&iterator)); )
bow_treenode_set_lambdas_uniform (leaf);
}
/* Set TN's mixture weights, LAMBDAS, to use only the estimates. */
void
bow_treenode_set_lambdas_leaf_only (treenode *tn)
{
int i;
tn->lambdas[0] = 1;
for (i = 1; i < tn->depth + 2; i++)
tn->lambdas[i] = 0;
}
/* Same as above, but for all leaves in the tree. */
void
bow_treenode_set_lambdas_leaf_only_all (treenode *tn)
{
treenode *iterator, *leaf;
assert (tn->parent == NULL);
for (iterator = tn; (leaf = bow_treenode_iterate_leaves (&iterator)); )
bow_treenode_set_lambdas_leaf_only (leaf);
}
/* Add WEIGHT to treenode TN's record of how much probability mass
document DI contributed to TN's NEW_WORDS for the word at DI's
WVI'th word. This mass can later be subtracted to do leave-one-out
calculations. DI_WV_NUM_ENTRIES-1 is the maximum WVI that can be
expected for DI; DI_COUNT-1 is the maximum DI that can be expected;
both are used to know how much space to allocate. */
void
bow_treenode_add_new_loo_for_di_wvi (treenode *tn,
double weight, int di, int wvi,
int di_wv_num_entries, int di_count)
{
int i;
if (tn->new_di_loo == NULL)
{
tn->new_di_loo =
bow_malloc (di_count * sizeof (double));
for (i = 0; i < di_count; i++)
tn->new_di_loo[i] = 0;
}
if (tn->new_di_wvi_loo == NULL)
{
tn->new_di_wvi_loo =
bow_malloc (di_count * sizeof (void*));
for (i = 0; i < di_count; i++)
tn->new_di_wvi_loo[i] = NULL;
}
if (tn->new_di_wvi_loo[di] == NULL)
{
tn->new_di_wvi_loo[di] =
bow_malloc (di_wv_num_entries * sizeof (double));
for (i = 0; i < di_wv_num_entries; i++)
tn->new_di_wvi_loo[di][i] = 0;
}
tn->new_di_loo[di] += weight;
tn->new_di_wvi_loo[di][wvi] += weight;
}
/* Clear all LOO info for treenode TN */
void
bow_treenode_free_loo (treenode *tn, int di_count)
{
int i;
/* For now, clear by freeing */
if (tn->di_loo)
{
bow_free (tn->di_loo);
tn->di_loo = NULL;
}
if (tn->di_wvi_loo)
{
for (i = 0; i < di_count; i++)
{
if (tn->di_wvi_loo[i])
bow_free (tn->di_wvi_loo[i]);
}
bow_free (tn->di_wvi_loo);
tn->di_wvi_loo = NULL;
}
}
/* Same as above, over all nodes of the tree. */
void
bow_treenode_free_loo_all (treenode *root, int di_count)
{
int ci;
bow_treenode_free_loo (root, di_count);
for (ci = 0; ci < root->children_count; ci++)
bow_treenode_free_loo_all (root->children[ci], di_count);
}
/* Clear all LOO info for treenode TN */
void
bow_treenode_free_loo_and_new_loo (treenode *tn, int di_count)
{
int i;
if (tn->di_loo)
{
bow_free (tn->di_loo);
tn->di_loo = NULL;
}
if (tn->new_di_loo)
{
bow_free (tn->new_di_loo);
tn->new_di_loo = NULL;
}
if (tn->di_wvi_loo)
{
for (i = 0; i < di_count; i++)
{
if (tn->di_wvi_loo[i])
bow_free (tn->di_wvi_loo[i]);
}
bow_free (tn->di_wvi_loo);
tn->di_wvi_loo = NULL;
}
if (tn->new_di_wvi_loo)
{
for (i = 0; i < di_count; i++)
{
if (tn->new_di_wvi_loo[i])
bow_free (tn->new_di_wvi_loo[i]);
}
bow_free (tn->new_di_wvi_loo);
tn->new_di_wvi_loo = NULL;
}
}
/* Same as above, over all nodes of the tree. */
void
bow_treenode_free_loo_and_new_loo_all (treenode *root, int di_count)
{
int ci;
bow_treenode_free_loo_and_new_loo (root, di_count);
for (ci = 0; ci < root->children_count; ci++)
bow_treenode_free_loo_and_new_loo_all (root->children[ci], di_count);
}
/* Set the leave-one-out information used for future BOW_TREENODE_PR_WI*()
calculations from the NEW_*_LOO variables, then clear the NEW_*_LOO
variables so they are ready for the next round. */
static void
bow_treenode_set_loo_from_new_loo (treenode *tn, int di_count)
{
bow_treenode_free_loo (tn, di_count);
tn->di_loo = tn->new_di_loo;
tn->di_wvi_loo = tn->new_di_wvi_loo;
tn->new_di_loo = NULL;
tn->new_di_wvi_loo = NULL;
}
/* Normalize the NEW_WORDS distribution, move it into the WORDS array
and zero the NEW_WORDS array. ALPHA is the parameter for the
Dirichlet prior. */
void
bow_treenode_set_words_from_new_words (treenode *tn, double alpha)
{
int wi;
double total_word_count = 0.0;
/* A special case for "Misc" nodes: increase their smoothing. NOTE:
This has no effect if MISC_STAYS_FLAT is non-zero. */
if (strstr (tn->name, "/Misc/"))
alpha++;
/* Calculate the normalizing constant */
for (wi = 0; wi < tn->words_capacity; wi++)
total_word_count += tn->new_words[wi];
total_word_count += alpha * tn->words_capacity;
//assert (total_word_count);
if (total_word_count == 0)
{
alpha = 1.0 / tn->words_capacity;;
total_word_count = 1.0;
}
for (wi = 0; wi < tn->words_capacity; wi++)
{
//assert (tn->new_words[wi] > 0);
assert (tn->new_words[wi] >= 0);
#if !USE_ACCELERATED_EM
#if !MISC_STAYS_FLAT
tn->words[wi] = (alpha + tn->new_words[wi]) / total_word_count;
#else
/* A special case for "Misc" nodes: they stay flat */
if (strstr (tn->name, "/Misc/"))
tn->words[wi] = 1.0 / tn->words_capacity;
else
tn->words[wi] = (alpha + tn->new_words[wi]) / total_word_count;
#endif /* MISC_STAYS_FLAT */
#else
tn->words[wi] =
(((1.0 - EM_ACCELERATION) * tn->words[wi])
+ (EM_ACCELERATION * (alpha + tn->new_words[wi]) / total_word_count));
if (tn->words[wi] < 0)
tn->words[wi] = 0;
#endif /* USE_ACCELERATED_EM */
assert (tn->words[wi] >= 0);
assert (tn->words[wi] <= 1);
tn->new_words[wi] = 0;
}
#if USE_ACCELERATED_EM
/* Renormalize after setting some to zero. */
total_word_count = 0;
for (wi = 0; wi < tn->words_capacity; wi++)
total_word_count += tn->words[wi];
for (wi = 0; wi < tn->words_capacity; wi++)
tn->words[wi] /= total_word_count;
#endif
/* Why was this conditioned on MISC_STAYS_FLAT?
The bow_treenode_pr_wi_loo_local function doesn't work with the
new_words_normalizer equal to zero! */
if (!MISC_STAYS_FLAT || !strstr (tn->name, "/Misc/"))
tn->new_words_normalizer = total_word_count;
else
tn->new_words_normalizer = 0;
/* Also roll over the LOO information. */
bow_treenode_set_loo_from_new_loo (tn, crossbow_docs->length);
}
/* Over all nodes of the tree, normalize the NEW_WORDS distribution,
move it into the WORDS array and zero the NEW_WORDS array. */
void
bow_treenode_set_words_from_new_words_all (treenode *root, double alpha)
{
int ci;
bow_treenode_set_words_from_new_words (root, alpha);
for (ci = 0; ci < root->children_count; ci++)
bow_treenode_set_words_from_new_words_all (root->children[ci], alpha);
}
/* Set NEW_WORDS counts to zero. */
void
bow_treenode_set_new_words_to_zero (treenode *tn)
{
int wi;
for (wi = 0; wi < tn->words_capacity; wi++)
tn->new_words[wi] = 0;
}
/* Same as above, over all nodes of the tree. */
void
bow_treenode_set_new_words_to_zero_all (treenode *root)
{
int ci;
bow_treenode_set_new_words_to_zero (root);
for (ci = 0; ci < root->children_count; ci++)
bow_treenode_set_new_words_to_zero_all (root->children[ci]);
}
/* Set the NEW_WORDS distribution from the addition of the WORDS
distribution and some random noise. NOISE_WEIGHT 0.5 gives equal
weight to the data and the noise. */
void
bow_treenode_set_new_words_from_perturbed_words (treenode *tn,
double noise_weight)
{
int wi;
for (wi = 0; wi < tn->words_capacity; wi++)
tn->new_words[wi] = ((1 - noise_weight) * tn->words[wi]
+ noise_weight * bow_random_01()/tn->words_capacity);
}
/* Same as above, over all nodes of the tree. */
void
bow_treenode_set_new_words_from_perturbed_words_all (treenode *root,
double noise_weight)
{
int ci;
bow_treenode_set_new_words_from_perturbed_words (root, noise_weight);
for (ci = 0; ci < root->children_count; ci++)
bow_treenode_set_new_words_from_perturbed_words_all (root->children[ci],
noise_weight);
}
/* Over all leaves of the tree, set the PRIOR by the results of
smoothing and normalizing the NEW_PRIOR distribution. ALPHA is the
parameter for the Dirichlet prior. */
void
bow_treenode_set_leaf_prior_from_new_prior_all (treenode *root, double alpha)
{
treenode *iterator, *leaf;
double prior_sum = 0;
assert (root->parent == NULL);
for (iterator = root; (leaf = bow_treenode_iterate_leaves (&iterator)); )
{
if (strstr (leaf->name, "/Misc/"))
{
/* Arbitrarily give /Misc/ node the same weight as the average
of the first two children of LEAF's parent. */
assert (leaf->parent->children_count >= 2);
leaf->new_prior = (leaf->parent->children[0]->new_prior
+ leaf->parent->children[1]->new_prior) / 2;
}
prior_sum += leaf->new_prior + alpha;
}
assert (prior_sum);
for (iterator = root; (leaf = bow_treenode_iterate_leaves (&iterator)); )
{
leaf->prior = (leaf->new_prior + alpha) / prior_sum;
leaf->new_prior = 0;
}
}
/* Over all nodes (including interior and root) of the tree, set the
PRIOR by the results of smoothing and normalizing the NEW_PRIOR
distribution. ALPHA is the parameter for the Dirichlet prior. */
void
bow_treenode_set_prior_from_new_prior_all (treenode *root, double alpha)
{
treenode *iterator, *leaf;
double prior_sum = 0;
assert (root->parent == NULL);
for (iterator = root; (leaf = bow_treenode_iterate_all (&iterator)); )
prior_sum += leaf->new_prior + alpha;
assert (prior_sum);
for (iterator = root; (leaf = bow_treenode_iterate_all (&iterator)); )
{
leaf->prior = (leaf->new_prior + alpha) / prior_sum;
leaf->new_prior = 0;
}
}
/* Over all nodes (including interior and root) of the tree, plus one
"extra" quantity (intended for the prior probability of the uniform
distribution), set the PRIOR by the results of smoothing and
normalizing the NEW_PRIOR distribution, and set EXTRA as part of
the normalization. ALPHA is the parameter for the Dirichlet
prior. */
void
bow_treenode_set_prior_and_extra_from_new_prior_all (treenode *root,
double *new_extra,
double *extra,
double alpha)
{
treenode *iterator, *leaf;
double prior_sum = 0;
assert (root->parent == NULL);
for (iterator = root; (leaf = bow_treenode_iterate_all (&iterator)); )
prior_sum += leaf->new_prior + alpha;
prior_sum += *new_extra + alpha;
assert (prior_sum);
for (iterator = root; (leaf = bow_treenode_iterate_all (&iterator)); )
{
leaf->prior = (leaf->new_prior + alpha) / prior_sum;
leaf->new_prior = 0;
}
*extra = (*new_extra + alpha) / prior_sum;
*new_extra = 0;
}
/* Normalize the NEW_LAMBDAS distribution, move it into the LAMBDAS array
and zero the NEW_LAMBDAS array. ALPHA is the parameter for the
Dirichlet prior. */
void
bow_treenode_set_lambdas_from_new_lambdas (treenode *tn, double alpha)
{
int ai;
double total_lambdas_count = 0.0;
#if MISC_STAYS_FLAT
if (strstr (tn->name, "/Misc/"))
{
bow_treenode_set_lambdas_uniform (tn);
for (ai = 0; ai < tn->depth + 2; ai++)
tn->new_lambdas[ai] = 0;
}
#endif
/* Calculate the normalizing constant */
for (ai = 0; ai < tn->depth + 2; ai++)
total_lambdas_count += tn->new_lambdas[ai];
total_lambdas_count += alpha * (tn->depth + 2);
//assert (total_lambdas_count);
if (total_lambdas_count == 0)
{
alpha = 1.0 / (tn->depth + 2);
total_lambdas_count = 1.0;
}
for (ai = 0; ai < tn->depth + 2; ai++)
{
assert (tn->new_lambdas[ai] >= 0);
#if 1 || !USE_ACCELERATED_EM
tn->lambdas[ai] = (alpha + tn->new_lambdas[ai]) / total_lambdas_count;
#else
tn->lambdas[ai] =
(((1.0 - EM_ACCELERATION) * tn->lambdas[ai])
+ (EM_ACCELERATION * (alpha + tn->new_lambdas[ai])
/ total_lambdas_count));
if (tn->lambdas[ai] < 0)
tn->lambdas[ai] = 0;
#endif
assert (tn->lambdas[ai] >= 0);
assert (tn->lambdas[ai] <= 1);
tn->new_lambdas[ai] = 0;
}
}
/* Set the CLASSES distribution to uniform, allocating space for it if
necessary */
void
bow_treenode_set_classes_uniform (treenode *tn, int classes_capacity)
{
int ci;
if (tn->classes_capacity == 0)
{
tn->classes_capacity = classes_capacity;
tn->classes = bow_malloc (classes_capacity * sizeof (double));
tn->new_classes = bow_malloc (classes_capacity * sizeof (double));
}
assert (classes_capacity == tn->classes_capacity);
for (ci = 0; ci < classes_capacity; ci++)
{
tn->classes[ci] = 1.0 / classes_capacity;
tn->new_classes[ci] = 0.0;
}
}
/* Normalize the NEW_CLASSES distribution, move it into the CLASSES array
and zero the NEW_CLASSES array. ALPHA is the parameter for the
Dirichlet prior. */
void
bow_treenode_set_classes_from_new_classes (treenode *tn, double alpha)
{
int ci;
double total_classes_count = 0;
assert (tn->classes_capacity > 0);
for (ci = 0; ci < tn->classes_capacity; ci++)
total_classes_count += tn->new_classes[ci];
total_classes_count += alpha * tn->classes_capacity;
if (total_classes_count == 0)
{
alpha = 1.0 / tn->classes_capacity;
total_classes_count = 1.0;
}
for (ci = 0; ci < tn->classes_capacity; ci++)
{
tn->classes[ci] = (alpha + tn->new_classes[ci]) / total_classes_count;
tn->new_classes[ci] = 0;
}
}
/* Return the log-probability of node TN's WORD distribution having
produced the word vector WV. */
double
bow_treenode_log_local_prob_of_wv (treenode *tn, bow_wv *wv)
{